
'''simple docstring'''

import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()







class       __lowerCAmelCase       (    _UpperCAmelCase			,   _UpperCAmelCase			,   unittest.TestCase							):
     snake_case  :       int				 = AutoencoderKL
     snake_case  :       int				 = 'sample'
     snake_case  :       str				 = 1e-2



     @property
     def        snake_case_					(self      ):
          _UpperCAmelCase   :    Tuple			    =       4
          _UpperCAmelCase   :    str			    =       3
          _UpperCAmelCase   :    Optional[int]			    =       (3_2, 3_2)

          _UpperCAmelCase   :    Tuple			    =       floats_tensor((batch_size, num_channels) + sizes      ).to(__UpperCamelCase      )

          return {"sample": image}



     @property
     def        snake_case_					(self      ):
          return (3, 3_2, 3_2)



     @property
     def        snake_case_					(self      ):
          return (3, 3_2, 3_2)



     def        snake_case_					(self      ):
          _UpperCAmelCase   :    int			    =       {
              """block_out_channels""": [3_2, 6_4],
              """in_channels""": 3,
              """out_channels""": 3,
              """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
              """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
              """latent_channels""": 4,
          }
          _UpperCAmelCase   :    List[Any]			    =       self.dummy_input
          return init_dict, inputs_dict



     def        snake_case_					(self      ):
          pass



     def        snake_case_					(self      ):
          pass



     @unittest.skipIf(torch_device == """mps"""		,      """Gradient checkpointing skipped on MPS"""      )
     def        snake_case_					(self      ):
          _UpperCAmelCase    ,	_UpperCAmelCase   :    Dict			    =       self.prepare_init_args_and_inputs_for_common()
          _UpperCAmelCase   :    Dict			    =       self.model_class(**__UpperCamelCase      )
          model.to(__UpperCamelCase      )

          assert not model.is_gradient_checkpointing and model.training

          _UpperCAmelCase   :    Tuple			    =       model(**__UpperCamelCase      ).sample
          # run the backwards pass on the model. For backwards pass, for simplicity purpose,
          # we won't calculate the loss and rather backprop on out.sum()
          model.zero_grad()

          _UpperCAmelCase   :    Optional[Any]			    =       torch.randn_like(__UpperCamelCase      )
          _UpperCAmelCase   :    Dict			    =       (out - labels).mean()
          loss.backward()

          # re-instantiate the model now enabling gradient checkpointing
          _UpperCAmelCase   :    Optional[Any]			    =       self.model_class(**__UpperCamelCase      )
          # clone model
          model_a.load_state_dict(model.state_dict()      )
          model_a.to(__UpperCamelCase      )
          model_a.enable_gradient_checkpointing()

          assert model_a.is_gradient_checkpointing and model_a.training

          _UpperCAmelCase   :    Optional[int]			    =       model_a(**__UpperCamelCase      ).sample
          # run the backwards pass on the model. For backwards pass, for simplicity purpose,
          # we won't calculate the loss and rather backprop on out.sum()
          model_a.zero_grad()
          _UpperCAmelCase   :    Union[str, Any]			    =       (out_a - labels).mean()
          loss_a.backward()

          # compare the output and parameters gradients
          self.assertTrue((loss - loss_a).abs() < 1e-5      )
          _UpperCAmelCase   :    int			    =       dict(model.named_parameters()      )
          _UpperCAmelCase   :    Optional[int]			    =       dict(model_a.named_parameters()      )
          for name, param in named_params.items():
               self.assertTrue(torch_all_close(param.grad.data		,      named_params_a[name].grad.data		,      atol=5e-5      )      )



     def        snake_case_					(self      ):
          _UpperCAmelCase    ,	_UpperCAmelCase   :    int			    =       AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy"""		,      output_loading_info=__UpperCamelCase      )
          self.assertIsNotNone(__UpperCamelCase      )
          self.assertEqual(len(loading_info["""missing_keys"""]      )		,      0      )

          model.to(__UpperCamelCase      )
          _UpperCAmelCase   :    Tuple			    =       model(**self.dummy_input      )

          assert image is not None, "Make sure output is not None"



     def        snake_case_					(self      ):
          _UpperCAmelCase   :    List[Any]			    =       AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy"""      )
          _UpperCAmelCase   :    Union[str, Any]			    =       model.to(__UpperCamelCase      )
          model.eval()

          if torch_device == "mps":
               _UpperCAmelCase   :    int			    =       torch.manual_seed(0      )
          else:
               _UpperCAmelCase   :    List[Any]			    =       torch.Generator(device=__UpperCamelCase      ).manual_seed(0      )

          _UpperCAmelCase   :    int			    =       torch.randn(
              1		,      model.config.in_channels		,      model.config.sample_size		,      model.config.sample_size		,      generator=torch.manual_seed(0      )		,      )
          _UpperCAmelCase   :    List[str]			    =       image.to(__UpperCamelCase      )
          with torch.no_grad():
               _UpperCAmelCase   :    Tuple			    =       model(__UpperCamelCase		,      sample_posterior=__UpperCamelCase		,      generator=__UpperCamelCase      ).sample

          _UpperCAmelCase   :    int			    =       output[0, -1, -3:, -3:].flatten().cpu()

          # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
          # the expected output slices are not the same for CPU and GPU.
          if torch_device == "mps":
               _UpperCAmelCase   :    Any			    =       torch.tensor(
                   [
                       -4.0_078e-01,
                       -3.8_323e-04,
                       -1.2_681e-01,
                       -1.1_462e-01,
                       2.0_095e-01,
                       1.0_893e-01,
                       -8.8_247e-02,
                       -3.0_361e-01,
                       -9.8_644e-03,
                   ]      )
          elif torch_device == "cpu":
               _UpperCAmelCase   :    Optional[int]			    =       torch.tensor(
                   [-0.1_3_5_2, 0.0_8_7_8, 0.0_4_1_9, -0.0_8_1_8, -0.1_0_6_9, 0.0_6_8_8, -0.1_4_5_8, -0.4_4_4_6, -0.0_0_2_6]      )
          else:
               _UpperCAmelCase   :    str			    =       torch.tensor(
                   [-0.2_4_2_1, 0.4_6_4_2, 0.2_5_0_7, -0.0_4_3_8, 0.0_6_8_2, 0.3_1_6_0, -0.2_0_1_8, -0.0_7_2_7, 0.2_4_8_5]      )

          self.assertTrue(torch_all_close(__UpperCamelCase		,      __UpperCamelCase		,      rtol=1e-2      )      )







@slow
class       __lowerCAmelCase       (    unittest.TestCase							):



     def        snake_case_					(self		,      lowerCAmelCase__		,      lowerCAmelCase__      ):
          return F"gaussian_noise_s={seed}_shape={'_'.join([str(__UpperCamelCase      ) for s in shape]      )}.npy"



     def        snake_case_					(self      ):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()



     def        snake_case_					(self		,      lowerCAmelCase__=0		,      lowerCAmelCase__=(4, 3, 5_1_2, 5_1_2)		,      lowerCAmelCase__=False      ):
          _UpperCAmelCase   :    int			    =       torch.floataa if fpaa else torch.floataa
          _UpperCAmelCase   :    str			    =       torch.from_numpy(load_hf_numpy(self.get_file_format(__UpperCamelCase		,      __UpperCamelCase      )      )      ).to(__UpperCamelCase      ).to(__UpperCamelCase      )
          return image



     def        snake_case_					(self		,      lowerCAmelCase__="CompVis/stable-diffusion-v1-4"		,      lowerCAmelCase__=False      ):
          _UpperCAmelCase   :    Optional[Any]			    =       """fp16""" if fpaa else None
          _UpperCAmelCase   :    List[Any]			    =       torch.floataa if fpaa else torch.floataa

          _UpperCAmelCase   :    Optional[int]			    =       AutoencoderKL.from_pretrained(
              __UpperCamelCase		,      subfolder="""vae"""		,      torch_dtype=__UpperCamelCase		,      revision=__UpperCamelCase		,      )
          model.to(__UpperCamelCase      ).eval()

          return model



     def        snake_case_					(self		,      lowerCAmelCase__=0      ):






          if torch_device == "mps":
               return torch.manual_seed(__UpperCamelCase      )
          return torch.Generator(device=__UpperCamelCase      ).manual_seed(__UpperCamelCase      )



     @parameterized.expand(
         [
             # fmt: off
             [3_3, [-0.1_6_0_3, 0.9_8_7_8, -0.0_4_9_5, -0.0_7_9_0, -0.2_7_0_9, 0.8_3_7_5, -0.2_0_6_0, -0.0_8_2_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
             [4_7, [-0.2_3_7_6, 0.1_1_6_8, 0.1_3_3_2, -0.4_8_4_0, -0.2_5_0_8, -0.0_7_9_1, -0.0_4_9_3, -0.4_0_8_9], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
             # fmt: on
         ]      )
     def        snake_case_					(self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__      ):
          _UpperCAmelCase   :    List[Any]			    =       self.get_sd_vae_model()
          _UpperCAmelCase   :    Any			    =       self.get_sd_image(__UpperCamelCase      )
          _UpperCAmelCase   :    Dict			    =       self.get_generator(__UpperCamelCase      )

          with torch.no_grad():
               _UpperCAmelCase   :    List[Any]			    =       model(__UpperCamelCase		,      generator=__UpperCamelCase		,      sample_posterior=__UpperCamelCase      ).sample

          assert sample.shape == image.shape

          _UpperCAmelCase   :    Tuple			    =       sample[-1, -2:, -2:, :2].flatten().float().cpu()
          _UpperCAmelCase   :    Tuple			    =       torch.tensor(expected_slice_mps if torch_device == """mps""" else expected_slice      )

          assert torch_all_close(__UpperCamelCase		,      __UpperCamelCase		,      atol=3e-3      )



     @parameterized.expand(
         [
             # fmt: off
             [3_3, [-0.0_5_1_3, 0.0_2_8_9, 1.3_7_9_9, 0.2_1_6_6, -0.2_5_7_3, -0.0_8_7_1, 0.5_1_0_3, -0.0_9_9_9]],
             [4_7, [-0.4_1_2_8, -0.1_3_2_0, -0.3_7_0_4, 0.1_9_6_5, -0.4_1_1_6, -0.2_3_3_2, -0.3_3_4_0, 0.2_2_4_7]],
             # fmt: on
         ]      )
     @require_torch_gpu
     def        snake_case_					(self		,      lowerCAmelCase__		,      lowerCAmelCase__      ):
          _UpperCAmelCase   :    Any			    =       self.get_sd_vae_model(fpaa=__UpperCamelCase      )
          _UpperCAmelCase   :    List[Any]			    =       self.get_sd_image(__UpperCamelCase		,      fpaa=__UpperCamelCase      )
          _UpperCAmelCase   :    List[str]			    =       self.get_generator(__UpperCamelCase      )

          with torch.no_grad():
               _UpperCAmelCase   :    List[Any]			    =       model(__UpperCamelCase		,      generator=__UpperCamelCase		,      sample_posterior=__UpperCamelCase      ).sample

          assert sample.shape == image.shape

          _UpperCAmelCase   :    Union[str, Any]			    =       sample[-1, -2:, :2, -2:].flatten().float().cpu()
          _UpperCAmelCase   :    str			    =       torch.tensor(__UpperCamelCase      )

          assert torch_all_close(__UpperCamelCase		,      __UpperCamelCase		,      atol=1e-2      )



     @parameterized.expand(
         [
             # fmt: off
             [3_3, [-0.1_6_0_9, 0.9_8_6_6, -0.0_4_8_7, -0.0_7_7_7, -0.2_7_1_6, 0.8_3_6_8, -0.2_0_5_5, -0.0_8_1_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
             [4_7, [-0.2_3_7_7, 0.1_1_4_7, 0.1_3_3_3, -0.4_8_4_1, -0.2_5_0_6, -0.0_8_0_5, -0.0_4_9_1, -0.4_0_8_5], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
             # fmt: on
         ]      )
     def        snake_case_					(self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__      ):
          _UpperCAmelCase   :    Dict			    =       self.get_sd_vae_model()
          _UpperCAmelCase   :    Tuple			    =       self.get_sd_image(__UpperCamelCase      )

          with torch.no_grad():
               _UpperCAmelCase   :    Dict			    =       model(__UpperCamelCase      ).sample

          assert sample.shape == image.shape

          _UpperCAmelCase   :    Any			    =       sample[-1, -2:, -2:, :2].flatten().float().cpu()
          _UpperCAmelCase   :    Optional[Any]			    =       torch.tensor(expected_slice_mps if torch_device == """mps""" else expected_slice      )

          assert torch_all_close(__UpperCamelCase		,      __UpperCamelCase		,      atol=3e-3      )



     @parameterized.expand(
         [
             # fmt: off
             [1_3, [-0.2_0_5_1, -0.1_8_0_3, -0.2_3_1_1, -0.2_1_1_4, -0.3_2_9_2, -0.3_5_7_4, -0.2_9_5_3, -0.3_3_2_3]],
             [3_7, [-0.2_6_3_2, -0.2_6_2_5, -0.2_1_9_9, -0.2_7_4_1, -0.4_5_3_9, -0.4_9_9_0, -0.3_7_2_0, -0.4_9_2_5]],
             # fmt: on
         ]      )
     @require_torch_gpu
     def        snake_case_					(self		,      lowerCAmelCase__		,      lowerCAmelCase__      ):
          _UpperCAmelCase   :    Tuple			    =       self.get_sd_vae_model()
          _UpperCAmelCase   :    int			    =       self.get_sd_image(__UpperCamelCase		,      shape=(3, 4, 6_4, 6_4)      )

          with torch.no_grad():
               _UpperCAmelCase   :    str			    =       model.decode(__UpperCamelCase      ).sample

          assert list(sample.shape      ) == [3, 3, 5_1_2, 5_1_2]

          _UpperCAmelCase   :    Dict			    =       sample[-1, -2:, :2, -2:].flatten().cpu()
          _UpperCAmelCase   :    str			    =       torch.tensor(__UpperCamelCase      )

          assert torch_all_close(__UpperCamelCase		,      __UpperCamelCase		,      atol=1e-3      )



     @parameterized.expand(
         [
             # fmt: off
             [2_7, [-0.0_3_6_9, 0.0_2_0_7, -0.0_7_7_6, -0.0_6_8_2, -0.1_7_4_7, -0.1_9_3_0, -0.1_4_6_5, -0.2_0_3_9]],
             [1_6, [-0.1_6_2_8, -0.2_1_3_4, -0.2_7_4_7, -0.2_6_4_2, -0.3_7_7_4, -0.4_4_0_4, -0.3_6_8_7, -0.4_2_7_7]],
             # fmt: on
         ]      )
     @require_torch_gpu
     def        snake_case_					(self		,      lowerCAmelCase__		,      lowerCAmelCase__      ):
          _UpperCAmelCase   :    Optional[int]			    =       self.get_sd_vae_model(fpaa=__UpperCamelCase      )
          _UpperCAmelCase   :    List[str]			    =       self.get_sd_image(__UpperCamelCase		,      shape=(3, 4, 6_4, 6_4)		,      fpaa=__UpperCamelCase      )

          with torch.no_grad():
               _UpperCAmelCase   :    Optional[Any]			    =       model.decode(__UpperCamelCase      ).sample

          assert list(sample.shape      ) == [3, 3, 5_1_2, 5_1_2]

          _UpperCAmelCase   :    List[Any]			    =       sample[-1, -2:, :2, -2:].flatten().float().cpu()
          _UpperCAmelCase   :    Tuple			    =       torch.tensor(__UpperCamelCase      )

          assert torch_all_close(__UpperCamelCase		,      __UpperCamelCase		,      atol=5e-3      )



     @parameterized.expand([(1_3,), (1_6,), (2_7,)]      )
     @require_torch_gpu
     @unittest.skipIf(not is_xformers_available()		,      reason="""xformers is not required when using PyTorch 2.0."""      )
     def        snake_case_					(self		,      lowerCAmelCase__      ):
          _UpperCAmelCase   :    int			    =       self.get_sd_vae_model(fpaa=__UpperCamelCase      )
          _UpperCAmelCase   :    Optional[Any]			    =       self.get_sd_image(__UpperCamelCase		,      shape=(3, 4, 6_4, 6_4)		,      fpaa=__UpperCamelCase      )

          with torch.no_grad():
               _UpperCAmelCase   :    Tuple			    =       model.decode(__UpperCamelCase      ).sample

          model.enable_xformers_memory_efficient_attention()
          with torch.no_grad():
               _UpperCAmelCase   :    List[Any]			    =       model.decode(__UpperCamelCase      ).sample

          assert list(sample.shape      ) == [3, 3, 5_1_2, 5_1_2]

          assert torch_all_close(__UpperCamelCase		,      __UpperCamelCase		,      atol=1e-1      )



     @parameterized.expand([(1_3,), (1_6,), (3_7,)]      )
     @require_torch_gpu
     @unittest.skipIf(not is_xformers_available()		,      reason="""xformers is not required when using PyTorch 2.0."""      )
     def        snake_case_					(self		,      lowerCAmelCase__      ):
          _UpperCAmelCase   :    List[str]			    =       self.get_sd_vae_model()
          _UpperCAmelCase   :    Optional[int]			    =       self.get_sd_image(__UpperCamelCase		,      shape=(3, 4, 6_4, 6_4)      )

          with torch.no_grad():
               _UpperCAmelCase   :    Optional[int]			    =       model.decode(__UpperCamelCase      ).sample

          model.enable_xformers_memory_efficient_attention()
          with torch.no_grad():
               _UpperCAmelCase   :    List[str]			    =       model.decode(__UpperCamelCase      ).sample

          assert list(sample.shape      ) == [3, 3, 5_1_2, 5_1_2]

          assert torch_all_close(__UpperCamelCase		,      __UpperCamelCase		,      atol=1e-2      )



     @parameterized.expand(
         [
             # fmt: off
             [3_3, [-0.3_0_0_1, 0.0_9_1_8, -2.6_9_8_4, -3.9_7_2_0, -3.2_0_9_9, -5.0_3_5_3, 1.7_3_3_8, -0.2_0_6_5, 3.4_2_6_7]],
             [4_7, [-1.5_0_3_0, -4.3_8_7_1, -6.0_3_5_5, -9.1_1_5_7, -1.6_6_6_1, -2.7_8_5_3, 2.1_6_0_7, -5.0_8_2_3, 2.5_6_3_3]],
             # fmt: on
         ]      )
     def        snake_case_					(self		,      lowerCAmelCase__		,      lowerCAmelCase__      ):
          _UpperCAmelCase   :    Tuple			    =       self.get_sd_vae_model()
          _UpperCAmelCase   :    Tuple			    =       self.get_sd_image(__UpperCamelCase      )
          _UpperCAmelCase   :    Optional[Any]			    =       self.get_generator(__UpperCamelCase      )

          with torch.no_grad():
               _UpperCAmelCase   :    Any			    =       model.encode(__UpperCamelCase      ).latent_dist
               _UpperCAmelCase   :    Optional[Any]			    =       dist.sample(generator=__UpperCamelCase      )

          assert list(sample.shape      ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

          _UpperCAmelCase   :    Optional[Any]			    =       sample[0, -1, -3:, -3:].flatten().cpu()
          _UpperCAmelCase   :    Any			    =       torch.tensor(__UpperCamelCase      )

          _UpperCAmelCase   :    str			    =       3e-3 if torch_device != """mps""" else 1e-2
          assert torch_all_close(__UpperCamelCase		,      __UpperCamelCase		,      atol=__UpperCamelCase      )




"""simple docstring"""



import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
a       :						Any       		=		logging.get_logger(__name__)


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = RobertaPreLayerNormConfig.from_pretrained(
        _A       ,   architectures=["""RobertaPreLayerNormForMaskedLM"""]   )

    # convert state_dict
    _UpperCAmelCase        = torch.load(hf_hub_download(repo_id=_A       ,   filename="""pytorch_model.bin"""   )   )
    _UpperCAmelCase        = {}
    for tensor_key, tensor_value in original_state_dict.items():
        # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
        if tensor_key.startswith("""roberta."""   ):
            _UpperCAmelCase        = """roberta_prelayernorm.""" + tensor_key[len("""roberta."""   ) :]

        # The original implementation contains weights which are not used, remove them from the state_dict
        if tensor_key.endswith(""".self.LayerNorm.weight"""   ) or tensor_key.endswith(""".self.LayerNorm.bias"""   ):
            continue

        _UpperCAmelCase        = tensor_value

    _UpperCAmelCase        = RobertaPreLayerNormForMaskedLM.from_pretrained(
        pretrained_model_name_or_path=_A       ,   config=_A       ,   state_dict=_A   )
    model.save_pretrained(_A   )

    # convert tokenizer
    _UpperCAmelCase        = AutoTokenizer.from_pretrained(_A   )
    tokenizer.save_pretrained(_A   )


if __name__ == "__main__":
 a       :						Tuple       		=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--checkpoint-repo''',
     default=None,
     type=str,
     required=True,
     help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 a       :						Union[str, Any]       		=		parser.parse_args()
 convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)